Dynamic Network Slicing for Congestion-Aware XR Latency Control
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Solution Overview
Problem
Existing wireless network communication solutions for cloud-based extended reality (XR) applications face challenges with latency and jitter due to network congestion, leading to suboptimal user experiences, and current network slicing mechanisms are static and cost-prohibitive, resulting in over-subscription and inefficient resource use.
Innovation Solution
Implementing time-bound dynamic slicing in wireless communication networks using AI-generated projections of network congestion and slice performance, allowing on-demand allocation of network resources based on specified delay and duration requirements, enabling granular control over slice characteristics and costs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If static network slicing is implemented for cloud-based XR content streaming, then network resources are allocated to support XR applications, but this leads to over-subscription and overuse of radio resources resulting in network congestion and increased latency
Solution Approach 1:
The patent implements dynamic network slicing where slice parameters such as bandwidth allocation, latency requirements, and resource limits are adjusted in real-time based on actual network conditions and application needs. The network slice manager continuously monitors congestion levels and dynamically reconfigures slice characteristics to prevent over-subscription while maintaining QoS for active XR sessions.
Solution Approach 2:
The system changes multiple parameters simultaneously including bandwidth allocation, latency thresholds, jitter limits, and resource reservation levels based on network congestion state. When congestion is detected, the system adjusts these parameters to reduce overall resource consumption while maintaining acceptable QoS for priority applications, thereby reducing latency without complete over-subscription.
2Loss of time
If mobile edge computing networks are built out to reduce delay, then transmission delay is reduced, but the solution is highly cost-prohibitive
Solution Approach 1:
The patent creates network slices that can be dynamically allocated for multiple purposes including XR content streaming, cloud gaming, video conferencing, and other low-latency applications. The same physical network infrastructure supports multiple virtual slices with different QoS requirements, eliminating the need for dedicated edge computing infrastructure for each application type while maintaining low transmission delay through optimized resource allocation.
Solution Approach 2:
Instead of building physical edge computing infrastructure, the system creates virtual copies of network resources through network slicing. These virtual slices replicate the functionality of dedicated edge networks by allocating specific bandwidth, latency guarantees, and resource reservations over the existing shared infrastructure, providing similar performance at fraction of the cost.
3Device complexity
If one network slice is provided for all XR applications, then resource allocation is simplified, but this leads to over-subscription and inefficient resource use
Solution Approach 1:
The patent segments the network into multiple specialized slices including low-latency slices for real-time XR, high-bandwidth slices for 8K streaming, medium-priority slices for cloud gaming, and best-effort slices for non-critical traffic. Each slice has dedicated resource allocations and QoS parameters optimized for its specific application type, preventing over-subscription while maintaining manageable complexity through automated slice management.
Data Source
AI summary
Systems, methods, and software are disclosed herein for time-bound dynamic slicing for wireless communication networks in various implementations. In an implementation, a computing device receives a request from an application for a network slice including a specified delay and a slice duration. The computing device determines a projected congestion based on a context of the network slice and the slice duration and identifies one or more candidate slices according to the specified delay and the projected congestion.